Performance evaluation of the Extended Kalman Filter and Unscented Kalman Filter

Abstract

The Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are methods usually applied in the sensor fusion for Unmanned Aerial Vehicles due to its nonlinear navigation equations. This paper presents a comparison between the two filters considering the position, velocity and attitude of the vehicle and the IMU bias. The simulation experiments are designed according to performance evaluation techniques for two trajectories and different state vectors. The results show that the EKF has a lower computational cost than UKF, but the latter provides smaller errors for most of the states. It also show that the bias estimation influences positively the solution granted by the EKF.